library(mice)     # Boys dataset
library(tidyverse)# All the good stuff
library(ggplot2)  # Plotting suite (actually included in tidyverse)

• We can process a lot of information quickly with our eyes
• Plots give us information about
  • Distribution / shape
  • Irregularities
  • Assumptions
  • Intuitions
• Summary statistics, correlations, parameters, model tests, p-values do not tell the whole story

Source: https://www.autodeskresearch.com/publications/samestats

Layered plotting based on the book The Grammar of Graphics by Leland Wilkinsons.

With ggplot2 you

1. provide the data
2. define how to map variables to aesthetics
3. state which geometric object to display
4. (optional) edit the overall theme of the plot

ggplot2 then takes care of the details

The package is extremely popular and well described.

1: Provide the data

boys |> 
  ggplot()

2: state which geometric object to display

boys |> 
  ggplot() |> 
  geom_point()

3: map variable to aesthetics

boys |> 
  ggplot() +
  geom_point(aes(x = hgt, y = wgt)

Create the plot

gg <-  boys |> 
  ggplot() +
  geom_point(aes(x=hgt, y=wgt),
             col="dark green" )

Add another layer (smooth fit line)

gg <- gg + 
  geom_smooth(aes(x = hgt, y = wgt),
              col = "dark blue")

Give it some labels and a nice look

gg <- gg + 
  labs(x = "Age", y = "BMI", title = "BMI trend for boys") +
  theme_minimal()

plot(gg)

• x
• y
• size
• colour
• fill
• opacity (alpha)
• linetype
• …

gg <- boys |>  
  filter(!is.na(reg)) |>  
  ggplot() +
  
  geom_point(aes(x = hgt, 
                 y = wgt, 
                 shape  = reg, 
                 colour = age),
             alpha = 0.5) +
  labs(title  = "Trend for boys",
       x      = "Height", 
       y      = "Weight", 
       shape    = "Region",
       colour = "Age") +
  theme_minimal()

plot(gg)

• geom_point

• geom_bar

• geom_line

• geom_smooth

• geom_histogram

• geom_boxplot

• geom_density

Easy with ggsave()

# save as pdf
ggssave("plot.pdf", myplot)

# save as png and specify dimensions 
ggssave("plot.png", myplot, width = 7, height = 5, units="in")

What is it
* R Markdown is a file format for making dynamic documents

Who is it for
* For communicating to decision makers, who want to focus on the conclusions, not the code behind the analysis.

• For collaborating with other data scientists (including future you!), who are interested in both your conclusions, and how you reached them (i.e. the code).

• As an environment in which to do data science, as a modern day lab notebook where you can capture not only what you did, but also what you were thinking.

• Documents (HTML, PDF, Word, RTF, Markdown)
• Presentations
  • ioslides (HTML)
  • Beamer (PDF)
  • PowerPoint (PowerPoint)
• Journals
  • elsevier_article _ jss_article
• Dashboards
  • Flexdashboard
• Websites and blogs
  • blogdown
• Books
  • bookdown (HTML, PDF, ePUB and Kindle books)

To create a new document:
- File - New File - RMarkdown
You can create a title and an output-format and then RStudio sets up the basics for you.

It contains three important types of content:

• An (optional) YAML header surrounded by —s.
• Chunks of R code surrounded by ```.
• Text mixed with simple text formatting like # heading and italics.

…but just push (ctrl+shift+K) the Knit icon.

YAML - “yet another markup language”

• Controls many “whole document” settings
• Possible to set document parameters
• Possible to specify bibliography

RStudio creates a YAML header when starting from scratch with File \(\rightarrow\) New File \(\rightarrow\) R Markdown…

Examples

• *italic* gives italic

• **bold** gives bold

• ~~Strikethrough~~ gives Strikethrough

• superscript^2^subscript~2~ gives superscript²/subscript₂

• $e^{i\pi}+1=0$ gives \(e^{i\pi}+1=0\)

• `r nrow(mice::boys)` gives 748

• Headers:

• # Level 1 header

• ## Level 2 header

* Blah Blah
* Blah

gives

• Blah Blah
  
• Blah

1. Blah Blah
2. Blah

gives

1. Blah Blah
   
2. Blah

To integrate R code into your document you create a code chunk (Short cut Ctrl+Alt+i).
By default it will show both your code and the result from the console. So the following codechunk in your Rmarkdown file:

```{r}
a <- 100
a*2
```

Will be printed in your document as:

a <- 100
a*2

## [1] 200

When writing your Rmarkdown-file you can run each line in your chunk with Ctrl+Enter or the entire chunk with Ctrl+Shift+Enter

Printing tables to markdown aren’t pretty. But with the package kableExtra it is easy to make readable tabels.

boys |>  
select(-gen, -phb, -tv) |> 
head()

##      age  hgt   wgt   bmi   hc   reg
## 3  0.035 50.1 3.650 14.54 33.7 south
## 4  0.038 53.5 3.370 11.77 35.0 south
## 18 0.057 50.0 3.140 12.56 35.2 south
## 23 0.060 54.5 4.270 14.37 36.7 south
## 28 0.062 57.5 5.030 15.21 37.3 south
## 36 0.068 55.5 4.655 15.11 37.0 south

boys |>  
select(-gen, -phb, -tv, -hc) |> 
head() |> 
knitr::kable(format = "html",
col.names = c("Age","Height","Weight","BMI", "Region"),
align = "ccccc",
caption = "The 5 first boys")

boys |>  
  select(-gen, -phb, -tv, -hc) |> 
  head() |> 
  knitr::kable(col.names = c("Age","Height","Weight","BMI", "Region"),
               align = "ccccc",
               caption = "The 5 first boys") |>  
  kable_styling(bootstrap_options = c("hover"),
                full_width = FALSE, position = "left") 

Rmarkdown will notice when a code chunk produces a figure.

boys |> 
  ggplot() +
  geom_point(aes(x = age,
                 y = hgt))

## Warning: Removed 20 rows containing missing values (`geom_point()`).

Sometimes you do not want to show your code, but only your results. Then you can use a chunk option.
The option that hides the code in the output is called echo

```{r echo = F}
a <- 100
a*2
```

## [1] 200

x marks the thing that the chunk will not do

RMarkdown

• Rmarkdown - An introduction: A short userfriendly introduction to Rmarkdown with videos and examples.
• Rmarkdown - Cheat sheet: A handy cheat sheet
• Rmarkdown - The definitive guide: A very thorough look at RMarkdown. No need to read everything, but if there is a subject you struggle with, tak a look at the index.

ggplot2

• ggplot2 - The book: The ultimate guide to ggplot written by Hadley Wickham who is also the author to the package. It is quite long, but if you want to truely understand it, this is the place to start.
• ggplot2 - Cheat sheet: A very useful cheat sheet
• sf - Maps in ggplot2: sf is a package that extends ggplot2 so it can make maps

• If you want to learn more about everything we have looked at the best place to go is probable the online-book R for Datascience.
  
• It is free and written by Hadley Wickham, who is the main author behind tidyverse.
  
• You can find it here: r4ds.had.co.nz/